Quantitative Results
Table REF  shows the experimental results on DAVIS2016 [1]} on different methods. Apart from the performance (measured by J mean), switches for online-fining, using optical-flow, dense CRF (CRF) and boundary snapping (BS) are also described. Meanwhile, the inference time is also shown. In particular, compared with most of the competitors, our algorithm shares the same or much faster processing time with superior performance regarding the segmentation accuracy. Please note that, some methods which use much stronger backbone networks are not listed out for the purpose of fair comparison.
Compared with OSVOS [2]}, for which the online fine-tuning is necessary, our method just takes a smaller fraction of time to do inference.
Compared to the baseline method PML  [3]} which use the same feature extractor, our method is twice faster and with better performance. Compared OSNM [4]}, with the same efficiency, our method achieve 3.4 percent improvements regarding to the segmentation accuracy.
